DeepObfuscator: Adversarial Training Framework for Privacy-Preserving Image Classification

By Ang Li, Jiayi Cora Guo, Huanrui Yang, Yiran Chen. 2019

45 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Li Fei-Fei
2. Vivek Sharma
3. Yi Yang
4. Luc Van Gool
5. Wanli Ouyang
6. Trevor Darrell
7. Jia Deng
8. Andrea Vedaldi

Citations in the corpus

• DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks (2020, methodology): “Considerations for balancing compute feasibility and privacy has motivated private collaborative inference schemes [24, 23] that split f(θ; ·) into f1(θ1; ·) and f2(θ2; ·) where:”
• NoPeek: Information leakage reduction to share activations in distributed deep learning (2020, background): “…loss based privacy: These can be categorized into hiding specific sensitive attributes using adversarial training that reaches an equilibrium based on information theoretic loss functions optimized under minimax settings through learning weights of deep learning models [29, 30, 31, 32, 33, 34, 35].”
• A Study of Face Obfuscation in ImageNet (2021, background): “For example, model inversion attack recovers sensitive attributes (e.g., gender) of an individual given the model’s output (Fredrikson et al., 2014; 2015; Hamm, 2017; Li et al., 2019; Wu et al., 2019).”